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Google is winning on every AI front

(www.thealgorithmicbridge.com)
993 points vinhnx | 1 comments | | HN request time: 0.328s | source
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thunderbird120 ◴[] No.43661807[source]
This article doesn't mention TPUs anywhere. I don't think it's obvious for people outside of google's ecosystem just how extraordinarily good the JAX + TPU ecosystem is. Google several structural advantages over other major players, but the largest one is that they roll their own compute solution which is actually very mature and competitive. TPUs are extremely good at both training and inference[1] especially at scale. Google's ability to tailor their mature hardware to exactly what they need gives them a massive leg up on competition. AI companies fundamentally have to answer the question "what can you do that no one else can?". Google's hardware advantage provides an actual answer to that question which can't be erased the next time someone drops a new model onto huggingface.

[1]https://blog.google/products/google-cloud/ironwood-tpu-age-o...

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noosphr ◴[] No.43661870[source]
And yet google's main structural disadvantage is being google.

Modern BERT with the extended context has solved natural language web search. I mean it as no exaggeration that _everything_ google does for search is now obsolete. The only reason why google search isn't dead yet is that it takes a while to index all web paged into a vector database.

And yet it wasn't google that released the architecture update, it was hugging face as a summer collaboration between a dozen people. Google's version came out in 2018 and languished for a decade because it would destroy their business model.

Google is too risk averse to do anything, but completely doomed if they don't cannibalize their cash cow product. Web search is no longer a crown jewel, but plumbing that answering services, like perplexity, need. I don't see google being able to pull off an iPhone moment where they killed the iPod to win the next 20 years.

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jampekka ◴[] No.43662862[source]
> Modern BERT with the extended context has solved natural language web search.

I doubt this. Embedding models are no panacea even with a lot simpler retrieval tasks like RAG.

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1. noosphr ◴[] No.43670314[source]
RAG is literally what Google Search is.

Unlike the natural language queries that RAG has to deal with, Google searches are (usually) atomic ideas and encoder-only models have a much easier time with them.